Menyiapkan percakapan

Sekarang Anda memiliki tool kustom untuk membantu menghitung panjang atap, Anda dapat mengatur keluaran agen agar merespons query yang Anda masukkan. Dengan sedikit memodifikasi pernyataan print, Anda dapat langsung membandingkan query dan respons agen untuk memastikan keakuratan. tools dan query Anda telah disiapkan dan model Anda siap digunakan.

Latihan ini adalah bagian dari kursus

Merancang Sistem Agentic dengan LangChain

Petunjuk latihan

Inisialisasi fungsi create_react_agent dengan membuat objek bernama app, lalu teruskan argumen yang diperlukan.
Panggil agen app, teruskan query Anda, lalu simpan dan cetak response dari agen.
Tetapkan "user_input" sebagai query dan "agent_output" sebagai item terakhir yang diambil dari "messages" dalam response sebelum keduanya dicetak.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

tools = [hypotenuse_length]
query = "What is the value of the hypotenuse for a triangle with sides 3 and 5?"

# Create the ReAct agent
app = ____(____, ____)

# Invoke the agent with a query and store the messages
response = ____.____({"messages": [("human", ____)]})

# Define and print the input and output messages
print({
    "____": ____,
    "____": ____["____"][____].____
})

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Merancang Sistem Agentic dengan LangChain

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

Build intelligent agentic systems! Discover the key components of LangChain agents, including how prompts, LLMs, and tools work together for reasoning and action. You'll set up an agent with OpenAI's API, define custom tools, and tackle real-world tasks like math calculations. Plus, explore how LangChain organizes data using graphs, nodes, and edges.

Exercise 1: Agen di LangChain Exercise 2: Membuat agen ReAct Exercise 3: Komponen agen LangChain Exercise 4: Membangun alat kustom Exercise 5: Buat tool untuk perhitungan matematika Exercise 6: Mengintegrasikan alat kustom dan kueri Exercise 7: Percakapan dengan agen ReAct Exercise 8: Menyiapkan percakapan

Latihan Saat Ini

Exercise 9: Ajukan pertanyaan tentang riwayat percakapan

Build dynamic, tool-augmented chatbots with LangChain and LangGraph! You’ll explore how to create a chatbot that adapts based on user input by defining states and integrating external APIs for real-time information retrieval. You'll connect these components into a responsive graph structure, enabling smooth transitions between conversation and tool-assisted responses. By the end, you’ll have a visually represented chatbot framework with enhanced reasoning and multi-step workflows.

Exercise 1: Building graphs for chatbots Exercise 2: Graph and agent states Exercise 3: Adding nodes and edges Exercise 4: Generating chatbot responses Exercise 5: Queries and responses Exercise 6: Generate a graph diagram Exercise 7: Adding external tools to a chatbot Exercise 8: Build a Wikipedia tool Exercise 9: Add a tool to a graph Exercise 10: Adding memory and conversation Exercise 11: Visualize the graph and return tool outputs Exercise 12: Adding graph memory Exercise 13: Using graph memory for conversation

Expand your chatbot with dynamic tools and memory! Define and integrate multiple tools into flexible workflows, build functions for dynamic tool calling, and configure your chatbot for multiple-tool handling. Organize memory and outputs to enable interleaved, multi-turn conversations. By the end, you'll have created a sophisticated chatbot capable of complex interactions.

Exercise 1: Defining multiple tools Exercise 2: Build a tool that invokes the LLM Exercise 3: Build a tool with Python code Exercise 4: Binding multiple tools Exercise 5: Defining nodes and edges for flexible function calling Exercise 6: Define a function that stops the chatbot Exercise 7: Create a function to return an LLM response Exercise 8: Create the graph workflow for multiple tools Exercise 9: Organize chatbot outputs with memory Exercise 10: Configure outputs for multiple tools Exercise 11: Enable multi-turn conversation with memory Exercise 12: Congratulations!